AI Funding Is Not Competitive — It’s Concentrated

Total venture funding by AI startup category (Feb 2025–Feb 2026). Foundation Models & LLMs raised $80B more than all other 13 categories combined.

Makeover Monday 2026 Week 8. Redesigning the Periodic Table of AI Startups using a grouped lollipop chart to expose extreme funding concentration: Foundation Models & LLMs captured $80B — more than all other 13 categories combined — averaging $5,333M per startup, 28x the average of every other category.

Original

The original visualization comes from Periodic Table of AI

Original visualization

Makeover

Figure 1: Horizontal lollipop chart showing total venture funding for 14 AI startup categories from February 2025 to February 2026, grouped by layer: Foundation, Horizontal AI, Vertical AI, and Emerging & Frontier. Foundation Models & LLMs dominate at $80B — more than all other 13 categories combined — averaging ~$5,333M per startup, 28 times the average of all other categories. AI Agents & Copilots is a distant second at $13B. Most categories received under $10B.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse, ggtext, showtext, scales, glue,
  janitor, lubridate
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 9,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

df_raw <- readxl::read_excel(
  here::here("data/MakeoverMonday/2026/Periodic Table of AI.xlsx")) |>
  clean_names()
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(df_raw)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |- Enrich ----
df_enriched <- df_raw |>
  mutate(
    group_label = factor(
      group_label,
      levels = c("Foundation Layer", "Horizontal AI", "Vertical AI", "Emerging & Frontier")
    ),
    is_fm = name == "Foundation Models & LLMs",
    funding_per_startup_m = round(funding_billions * 1000 / startup_count, 0)
  )

### |- Key stats for annotations ----
fm_fps <- df_enriched |>
  filter(is_fm) |>
  pull(funding_per_startup_m)

others_fps <- df_enriched |>
  filter(!is_fm) |>
  summarise(avg = round(mean(funding_per_startup_m), 0)) |>
  pull(avg)

multiplier <- round(fm_fps / others_fps, 0)

### |- Plot data ----
plot_data <- df_enriched |>
  group_by(group_label) |>
  arrange(funding_billions, .by_group = TRUE) |>
  ungroup() |>
  mutate(
    name_within = paste0(name, "___", group_label),
    name_within = fct_inorder(name_within),
    highlight = if_else(is_fm,
                        "Foundation Models & LLMs",
                        "All other categories"
    ),
    funding_label = if_else(
      funding_billions >= 3,
      paste0("$", funding_billions, "B"),
      NA_character_
    )
  )

### |- Annotation data ----
ann_line1 <- tibble(
  group_label = factor("Foundation Layer", levels = levels(plot_data$group_label)),
  x = 18, y_num = 3,
  label = glue("~${fm_fps}M per startup")
)
ann_line2 <- tibble(
  group_label = factor("Foundation Layer", levels = levels(plot_data$group_label)),
  x = 18, y_num = 2.58,
  label = glue("vs. ~${others_fps}M avg for all other categories ({multiplier}x more)")
)
ann_line3 <- tibble(
  group_label = factor("Foundation Layer", levels = levels(plot_data$group_label)),
  x = 46, y_num = 1.70,
  label = "Led by OpenAI ($40B in a single round)"
)
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    highlight  = "#5C2D91",  
    default    = "#9BA8B5",   
    annotation = "#3D1A6E",   
    note       = "#6B7280",  
    grid       = "#EFEFEF",
    text_dark  = "#1A1A2E",
    text_mid   = "#4A4A6A",
    text_light = "#9A9AB0"
  )
)

### |- titles and caption ----
title_text    <- "AI Funding Is Not Competitive \u2014 It\u2019s Concentrated"

subtitle_text <- glue(
  "Total venture funding by AI startup category (Feb 2025\u2013Feb 2026). ",
  "Foundation Models & LLMs raised **$80B**<br>more than all other 13 categories combined."
)

caption_text <- create_mm_caption(
  mm_year     = 2026,
  mm_week     = 8,
  source_text = "Voronoi App<br>**Note:** Funding per startup = total funding / startup count"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.3), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.8), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    # axis.line.x  = element_line(color = "#252525", linewidth = .1),
    # axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    # panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    # panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    panel.grid.major.x = element_line(color = colors$palette$grid, linewidth = 0.35),
    panel.grid.major.y = element_blank(),
    panel.grid.minor   = element_blank(),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- Final Plot ----
p <- ggplot(plot_data, aes(x = funding_billions, y = name_within)) +
  # Geoms
  geom_segment(
    aes(x = 0, xend = funding_billions, yend = name_within, color = highlight),
    linewidth = 0.85,
    show.legend = FALSE
  ) +
  geom_point(
    aes(color = highlight),
    size = 4.5,
    show.legend = FALSE
  ) +
  geom_text(
    aes(label = funding_label),
    nudge_x = 4.5,
    size = 3.0,
    color = colors$palette$text_mid,
    fontface = "bold",
    family = fonts$text,
    na.rm = TRUE
  ) +
  geom_text(
    data = ann_line1,
    aes(x = x, y = y_num, label = label),
    inherit.aes = FALSE,
    color = colors$palette$annotation,
    size = 3.3,
    fontface = "bold",
    hjust = 0,
    family = fonts$text
  ) +
  geom_text(
    data = ann_line2,
    aes(x = x, y = y_num, label = label),
    inherit.aes = FALSE,
    color = colors$palette$note,
    size = 2.85,
    hjust = 0,
    family = fonts$text
  ) +
  geom_text(
    data = ann_line3,
    aes(x = x, y = y_num, label = label),
    inherit.aes = FALSE,
    color = colors$palette$annotation,
    size = 2.85,
    fontface = "italic",
    hjust = 1,
    family = fonts$text
  ) +
  # Facet
  facet_grid(
    group_label ~ .,
    scales = "free_y",
    space  = "free_y",
    switch = "y"
  ) +
  # Scales
  scale_color_manual(
    values = c(
      "Foundation Models & LLMs" = colors$palette$highlight,
      "All other categories"     = colors$palette$default
    )
  ) +
  scale_y_discrete(
    labels = function(x) sub("___.*$", "", x),
    expand = expansion(add = c(0.5, 1.2))
  ) +
  scale_x_continuous(
    labels = label_dollar(suffix = "B", accuracy = 1),
    breaks = c(0, 10, 20, 40, 60, 80),
    expand = expansion(mult = c(0, 0.12))
  ) +
  # Labs
  labs(
    title    = title_text,
    subtitle = subtitle_text,
    x        = "Total Funding (USD Billions)",
    y        = NULL,
    caption  = caption_text
  ) +
  # Theme
  theme(
    # Facet strips — left side, clean
    strip.placement = "outside",
    strip.text.y.left = element_text(
      angle  = 0,
      hjust  = 1,
      face   = "bold",
      size   = 8.5,
      color  = colors$palette$text_mid
    ),
    strip.background = element_blank(),
    
    # Spacing between facets
    panel.spacing.y = unit(12, "pt"),
    plot.title = element_markdown(
      size = rel(1.6),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 0, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.85),
      family = fonts$subtitle,
      face = "italic",
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.5,
      margin = margin(t = 5, b = 25)
    ),
    plot.caption = element_markdown(
      size = rel(0.5),
      family = fonts$subtitle,
      color = colors$caption,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(t = 20, b = 5)
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 9
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      janitor_2.2.1   glue_1.8.0      scales_1.4.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.0    
[13] purrr_1.2.1     readr_2.1.6     tidyr_1.3.2     tibble_3.3.1   
[17] ggplot2_4.0.2   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.47          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.1        tools_4.4.0        generics_0.1.3     curl_5.2.1        
 [9] gifski_1.32.0-2    pkgconfig_2.0.3    RColorBrewer_1.1-3 S7_0.2.1          
[13] readxl_1.4.5       lifecycle_1.0.5    compiler_4.4.0     farver_2.1.2      
[17] textshaping_0.3.7  codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[21] yaml_2.3.10        pillar_1.11.1      camcorder_0.1.0    magick_2.9.0      
[25] commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.3     
[29] rsvg_2.6.0         rprojroot_2.1.1    fastmap_1.2.0      grid_4.4.0        
[33] cli_3.6.5          magrittr_2.0.4     withr_3.0.1        timechange_0.4.0  
[37] rmarkdown_2.28     cellranger_1.1.0   ragg_1.5.0         hms_1.1.4         
[41] evaluate_1.0.0     knitr_1.48         markdown_1.12      rlang_1.1.7       
[45] gridtext_0.1.5     Rcpp_1.1.1         xml2_1.5.2         svglite_2.2.2     
[49] rstudioapi_0.18.0  jsonlite_2.0.0     R6_2.5.1           systemfonts_1.3.1 

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_08.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 8: The Periodic Table of AI Startups 2. Original Article: The Periodic Table of AI Startups — 14 categories of AI companies founded/funded Feb 2025–Feb 2026 - Source: Voronoi App - Coverage: 14 AI startup categories, February 2025–February 2026

Source Data: 3. Dataset: 2026 Week 8 — The Periodic Table of AI Startups - Source: Voronoi App via data.world/makeovermonday - Data includes: Category name, symbol, group layer, total funding ($B), startup count, YoY growth (%), and trend label - Categories covered: Foundation Layer, Horizontal AI, Vertical AI, Emerging & Frontier - Period: February 2025–February 2026

Note: One derived metric was computed for this visualization: funding per startup (total funding × 1,000 / startup count), expressed in USD millions. No other transformations were applied to the source data.

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils